System and method for insurance underwriting and rating

ABSTRACT

An apparatus and method for underwriting and/or rating an insurance policy, and for generating information for underwriting and/or rating an insurance policy, are provided. The system and method may include identifying a vehicle, determining a value of at least one vehicle history data variable or a group of variables, and underwriting and/or rating the policy based on the value of the at least one vehicle history data variable or a group of variables. The system and method may also include generating a score based on the value of the at least one vehicle history data variable or group of variables.

CROSS REFERENCE TO RELATED DOCUMENTS

This application claims priority to provisional patent application No.60/907,899 entitled “System and Method for Insurance Underwriting andRating,” filed Apr. 20, 2007, the entire disclosure of which is herebyincorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to a system and method of insuranceunderwriting and rating vehicles. In particular, the present inventionis directed to such system and method in which insurance underwritingand rating are provided based on vehicle history attributes.

2. Description of Related Art

The vehicle industry is perhaps one of the largest industries in manyindustrialized regions of the world. As a result, the market for usedvehicles, and especially automobiles, has evolved into a substantialmarket, especially in North America, and in particular, the UnitedStates and Canada.

Those parties involved in insuring used vehicles recognize the value ofinformation relating to a few specific facts related to a vehicle inmaking a decision to write a new policy and determining the price ofthat policy. Consequently, services have been created that functionprimarily to provide certain limited vehicle history information tovarious parties in the used vehicle market, including, for example,insurance providers.

When an insurance company is writing a new policy, they first“underwrite” that policy to determine whether the risk is one they arewilling to accept or not, and if so, to which class of policies itbelongs. Many insurers have both “standard” and “nonstandard” policyclasses with the less risky policies going into the standard class andthe riskier policies (e.g., teenage drivers) going into the nonstandardclass. That being said, some insurers have only one policy class andsome have multiple policy classes depending on how they have elected tostructure their underwriting criteria.

Once the insured is assigned to a class through underwriting, theinsurance company then “rate” that policy based upon a set of criteriato determine a specific premium to be charged. Traditionally, thiscriteria has revolved around the class of vehicle (e.g., sports car vs.family sedan; 8 cylinder vs. 4 cylinder), driver behavior (e.g., numberof speeding tickets, the expected annual miles to be driven), and drivercharacteristics (e.g., age, gender).

Most insurers will not underwrite a vehicle that has previously receivedcertain title brands due to concerns about its structural integrity. Forexample, if the vehicle has been in a flood, then its wiring may be morelikely to malfunction potentially leading to an accident. Likewise, ifthe vehicle has previously sustained major damage and is in a subsequentaccident, insurers may believe that the new damage is likely to beamplified due to a weakened structure or the previous use of aftermarketparts.

Therefore, many insurers may require the insured to inform them if thetitle of the vehicle has been branded. If an insured fails to make thisdisclosure and the insurance company discovers this failure during theevaluation of a claim, the insurance company may elect to modify itsclaim payments or deny the claim in its entirety. Thus, the issue ofbranded title vehicles has historically been handled on the back end ofthe insurance process.

Some insurers have experienced enough of a problem with unwittinglyinsuring branded title vehicles that they have instituted a process forchecking the vehicle's title prior to issuing a policy. For example, aninsurance provider may run a CARFAX Vehicle History Report on everyvehicle as a part of their underwriting process. If the vehicle isdetermined to have a particular title brand, the insurance provider maydecline to insure the vehicle.

Many insurance providers utilize the title brand data for underwritingonly. This data is generally accessed through association with theparticular Vehicle Identification Number (VIN). For example, aninsurance company may decide not to insure a vehicle having a salvage orflood title brand. Also, some insurers use the expected annual miles tobe driven in the future as one of the criteria for determining a premiumfor an insurance policy. In fact, California law now requires insurancecompanies operating in California to use the aforementioned annualmileage as one of the rating factors used to determine policy premiums.

However, relying solely on prospective annual mileage information maynot be desirable, because historically, there has been no reliablesource for providing this data. Many insurers using this data rely onthe insured to self report their expected annual miles to be driven orrely on a crude statistical estimation model based upon the distancedriven between home and work. There are also other statistical modelsemployed, such as random sampling and “area rating”, where it is assumedthat everyone in the same geographic area drives the same amount yearlyunless otherwise noted.

Therefore, there exists an unfulfilled need for a system and method forunderwriting insurance policies and providing a system and method forrating underwritten insurance policies. In addition, there also existsan unfulfilled need for such a system and method in which underwritingis provided based on vehicle history events related to a specificvehicle. Furthermore, there also exists an unfulfilled need for a systemand method for underwriting insurance policies and providing a systemand method for rating underwritten insurance policies from informationrelated specifically to a particular vehicle based on of that specificvehicle's history.

SUMMARY OF THE INVENTION

In accordance with an embodiment of the present invention, a method ofunderwriting an insurance policy includes identifying a vehicle, formingat least one data grouping including at least one vehicle history datavariable of the identified vehicle, and processing the at least onevehicle history data variable of the at least one data grouping. Themethod may also include determining an overall value of the at least onedata grouping and underwriting an insurance policy for the identifiedvehicle based on said overall value of the at least one data grouping.

In accordance with another embodiment of the present invention, a methodof rating an insurance policy includes identifying a vehicle, forming atleast one data grouping including of least one vehicle history datavariable of the identified vehicle, and processing the at least onevehicle history data variable of the at least one data grouping. Themethod may further include determining an overall value of the at leastone data grouping and rating an insurance policy for the identifiedvehicle based on the overall value of the at least one data grouping.

In accordance with yet another embodiment of the present invention, amethod of determining insurability of a vehicle includes identifying avehicle, forming at least one data grouping including at least onevehicle history data variable of the identified vehicle, and processingthe at least one vehicle history data variable of the at least one datagrouping. The method may also include determining an overall value ofthe at least one data grouping and calculating insurability of theidentified vehicle based on the overall value of the at least one datagrouping.

In yet another embodiment of the present invention, an apparatus forunderwriting an insurance policy includes a means for identifying avehicle, a means for forming at least one data grouping including atleast one vehicle history data variable of the identified vehicle, and ameans for processing the at least one vehicle history data variable ofthe at least one data grouping. The apparatus may further include ameans for determining an overall value of the at least one data groupingand a means for underwriting an insurance policy for the identifiedvehicle based on the overall value of the at least one data grouping.

In still another embodiment of the present invention, an apparatus forrating an insurance policy includes a means for identifying a vehicle, ameans for forming at least one data grouping including of least onevehicle history data variable of the identified vehicle, and a means forprocessing the at least one vehicle history data variable of the atleast one data grouping. The apparatus may further include a means fordetermining an overall value of the at least one data grouping and ameans for rating an insurance policy for the identified vehicle based onsaid overall value of the at least one data grouping.

In yet another embodiment of the present invention, an apparatus fordetermining insurability of a vehicle includes a means for identifying avehicle, a means for forming at least one data grouping including atleast one vehicle history data variable of the identified vehicle, and ameans for processing the at least one vehicle history data variable ofthe at least one data grouping. The apparatus may also include a meansfor determining an overall value of the at least one data grouping and ameans for calculating insurability of the identified vehicle based onsaid overall value of the at least one data grouping.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a general schematic illustration of a vehicle historyinformation system in accordance with one embodiment of the presentinvention.

FIG. 2 is a detailed schematic illustration of the vehicle historyinformation system in accordance with one implementation of the presentinvention.

FIG. 3 provides a diagrammatic view of accumulated data for identifyinga vehicle according to one disclosed embodiment.

FIGS. 4-9 provide diagrammatic views of various data groupings accordingto exemplary disclosed embodiments.

FIG. 10 illustrates a flowchart providing a general overview of anexemplary insurance underwriting and rating process.

FIG. 11 illustrates a flowchart of an exemplary insurance underwritingand rating transaction according to one disclosed embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

FIG. 1 is a schematic diagram of a system in accordance with one exampleembodiment of the present invention which can be used to underwrite aninsurance policy and, where a decision is made to write a policy, toprovide a rating of the insurance policy. Initially, it should beunderstood that the term “insurability” may be used to describe thecapability to underwrite an insurance policy and, in cases where adecision is made to write a policy, to rate the insurance policy basedupon information provided by embodiments of the present invention. Also,the term “underwriting” encompasses both the act of deciding whether ornot to offer a policy to an applicant/vehicle as well as the act ofassigning an applicant/vehicle to a particular policy class based uponthe risk posed by the applicant/vehicle. Thus when underwriting a policyfor an applicant, the insurance company may elect to assign theapplicant/vehicle to a preferred policy class, assign theapplicant/vehicle to an at risk policy class, or decline to write apolicy for the applicant/vehicle altogether. The term “rating”encompasses determining an applicants policy premium within theirassigned class based upon specific variables pertaining to theapplicant/vehicle.

It is further understood that the term “vehicle” is used broadly hereinto encompass a variety of transportation devices. For example, vehiclesinclude automobiles of all types, motorized cycles including motorcyclesand all terrain vehicles, boats, watercraft, airplanes, etc. In thisregard, the present invention may be implemented in the manner describedto determine insurability for such vehicles. Thus, although the exampleembodiment discussed in detail below focuses on automobiles, it shouldbe understood that the present invention is not limited thereto but maybe implemented to determine insurability for any vehicle. Also, aninsurance policy may include one or any number of vehicles and themethod of the present invention may result in dividing the policy intomultiple policies to permit a policy to be written on one or moreapplicants/vehicles and not on others, for assigning the vehicles todifferent policy classes based on risk, and/or rating the policiesdifferent based on risk. Moreover, the present invention may beimplemented to determine the insurability of any other insurancepolicies, such as home, life, or other insurable asset, based on thevehicle history data variables, and/or groups thereof, as discussed morefully hereinbelow.

FIG. 1 is a schematic diagram of a system, for example in the form of anetworked computer system 10, designed to implement one embodiment ofthe subject invention. FIG. 1 may also be viewed as showing therelationship of the different entities potentially involved in theapplication of the present invention. Specifically, a computerimplemented vehicle history information system 12 exchanges data withone or more remote terminals 14 through data transmission across adistributed network 16, e.g. Internet. Alternatively, the one or moreremote terminals 14 may communicate directly with the vehicle historyinformation system 12. The terminals 14 are associated with an entity(e.g., an insurance company) accessing vehicle history informationsystem 12, as discussed more fully herein below, to obtain vehiclehistory information for underwriting and rating insurance policies.

The vehicle history information system 12 may be linked to one or morevehicle history data sources or suppliers that allow the vehicle historyinformation system administrator to receive and update vehicle historyinformation in system 12. The vehicle history data supplier may beindividual consumers, vehicle dealers, state titling offices, Departmentof Motor Vehicles, auto auctions and/or any other source of vehicleinformation.

The terminals 14 may be in communication with the vehicle historyinformation system 12 via distributed network 16. The distributednetwork 16 may be any type of communications channel such as a localarea network (LAN), wide area network (WAN), and/or direct computerconnections, and (or be implemented using) wireless connections, radiofrequency, infrared, or other wireless technologies using anyappropriate communication hardware and protocols, and may further be theInternet. Thus, terminals 14 may be connected to distributed network 16by any communication links 18, including hardwired and/or wirelesslinks.

FIG. 2 illustrates in more detail the vehicle history information system12 in accordance with one example embodiment of the present invention.Generally, vehicle history information system 12 may be implemented withany type of appropriate hardware and software, and can be embodied ascomputer readable storage media having executable instructions, and/or acomputer architecture as discussed herein below. Vehicle historyinformation system 12 may be implemented using a servers personalcomputer, a portable computer, a thin client, or any other computingdrive, such as a handset, or any combination of such devices. In thisregard, vehicle history information system 12 may be a single device ata single location as shown, or multiple devices at a single location, ormultiple locations that are connected together using any appropriatecommunication protocols over any communication medium.

FIG. 2 also illustrates in more detail the preferred implementation ofthe terminals 14. Although only two terminals are shown in detail as thecustomer terminals which represent the entities (e.g., insurancecompanies) of FIG. 1, it should be appreciated that any number ofterminals 14 may be implemented in communication with the distributednetwork 16. Terminal 14 may be any appropriate device for accessingvehicle history information system 12 such as a personal computer,portable computer, thin client, a handheld device such as a mobile phonehandset or PDA, and the like. Terminal 14 includes an input device 22and an output device 24 which allow the user of the terminal 14 toprovide information to, and receive information from, the vehiclehistory information system 12 via the distributed network 16. The inputdevice 22 may include a keyboard, mouse, etc. as well as memory devicesbased on magnetic, optical and/or solid state technologies includingdisc drives, CD/DVD drives, flash memory, etc. The output device 24 mayinclude a monitor screen, printer, etc. that allow the user of theterminal 14 to obtain the vehicle history attribute information fromvehicle history information system 12. The terminal 14 or customerlocation may also include an underwriting and rating module 25 adaptedto process information received from the vehicle history informationsystem 12 and generate information for making a decision regardingunderwriting and/or rating, or process the received information togenerate a decision regarding underwriting and/or rating, discussed morefully hereinbelow. The output of the underwriting and rating module 25and/or the information received from the vehicle history informationsystem 12 may be displayed by the output device 24.

Referring again to FIG. 2, in the preferred embodiment, vehicle historyinformation system 12 includes a vehicle history data analysis unit 26,a vehicle history database 30, and a communications managing module 33,all of which are connected together for effective data communication.Vehicle history data analysis unit 26, in the implementation shown,includes a vehicle history report module 35, a data determination module36, and a user interface module 42, the functions of each being furtherdescribed herein below.

Vehicle history database 30 contains a plurality of vehicle historydatasets which are collections of vehicle history data arranged,organized, indexed and/or retrievable based on a unique indicator suchas the unique vehicle identification number (such as VIN forautomobiles) of a particular vehicle. Each vehicle sold within theUnited States and most foreign countries has a unique identificationnumber which is identified on nearly every vehicle title issued andphysically identified on the respective vehicle. The identification canbe used to identify and trace the public record of each particularvehicle and to associate different vehicle data collected from a varietyof sources with the particular vehicle.

It should be noted that the vehicle history information system 12 andthe vehicle history data analysis unit 26 in accordance with theembodiment of the present invention is illustrated and discussed hereinas having various modules which perform particular functions. It shouldbe understood that these modules are merely schematically illustratedbased on their function and do not necessarily represent specifichardware or software. In this regard, these modules, units and othercomponents may be hardware and/or software implemented to substantiallyperform their particular functions explained herein. The variousfunctions of the different modules and units can be combined orsegregated as hardware and/or software modules in any manner, and can beused separately or in combination. Thus, the present invention asschematically embodied in FIG. 2 should not be construed to limit thevehicle history information system 12 of the present invention.

It should be clarified that as used herein, the term “vehicle” generallyrefers to only one particular, physical vehicle associated with a singleidentification number and does not refer to general model levelinformation or categories of vehicles. Such general model levelinformation relating to a specific make, model and/or year, is referredto as “type” of vehicle herein. Thus, the vehicle history database 30has a plurality of vehicle history datasets related to a plurality ofvehicles, each vehicle history dataset being related to a particularvehicle and having vehicle history attributes regarding the vehicle asdescribed below.

As previously mentioned, the administrator of vehicle historyinformation system 12 acquires vehicle history datasets from a varietyof data suppliers. The vehicle history datasets from the vehicle historydata supplier which are entered into vehicle history database 30 areassociated with a particular identification number and thus, aparticular vehicle. The vehicle data forming the vehicle historydatasets are added as records to vehicle history database 30 and indexedby the identification number. Therefore, the vehicle history datasetsstored in the vehicle history database 30 preferably include storedvehicle history attributes for a multitude of vehicles. The vehiclehistory datasets may be utilized by the vehicle history informationsystem 12 in any appropriate manner. For example, the vehicle historydatasets may be utilized by the vehicle history report module 35 togenerate a report by retrieving vehicle history attributes associatedwith the requested identification number of a particular vehicle. Someexamples of vehicle history attributes include accident information,branded title information (such as salvage title), police accidentreport and damage disclosure information, mileage information (such asodometer problems and actual mileage listings), title/registrationevents (including government registration, taxi registration andcommercial registration), stolen vehicle information, fleet information,emissions and safety inspection information, recall information, numberof owners, and any other information relevant to the history of thevehicle.

Vehicle history database 30 may be any appropriately implementeddatabase capable of effectively storing vehicle history datasets in anorganized accessible manner to permit efficient easy access to desiredpieces of data, e.g. one or more records associated with a particularidentification number, using appropriate database management systemsoftware. Preferably, vehicle history database 30 receives informationfrom, and may be accessed by, various components of vehicle historyinformation system 12.

Applicants have determined that a correlation exists between certainhistorical attributes of a vehicle and the likelihood of further damage,the severity of further damage, and/or the cost of repair to the vehiclein the future. Applicants' findings demonstrate that for certainhistorical events of a particular vehicle, subsequent damage is likelyto occur and/or the severity of the damage and/or the cost of repair islikely to be greater. These historical attributes or events are referredto herein as insurability data variables and are unique to a particularvehicle. Thus, throughout the description of this disclosure, theinsurability data variables may also be referred to as vehicle historydata variables. Hence, the present invention recognizes that theprospect of future vehicle damage and potential repair expense based onspecific historic events may affect the insurability of a particularvehicle. Further advantages of the present invention include determiningwhich vehicle history attributes correlate to the prospect of furthervehicle damage and associated repair costs. It is noted that theinsurability data variables may include, for example, only a portion ofall available vehicle history attributes in database 30.

For example, an insurance company may determine that it is notcost-effective to insure a vehicle having a certain vehicle history,because that vehicle's history suggests costly future damages in theevent of a subsequent accident. Again, the vehicle history for aspecific vehicle is qualified by that vehicles' unique vehicle historydata variables. The insurability of the particular vehicle may bedirectly attributed to these aforementioned vehicle history datavariables. The use of the insurability data variables, as disclosedherein, in the insurance underwriting and rating industry process canaffect a used vehicle's insurability.

FIGS. 4-9 show a listing of numerous vehicle history data variables,identified as insurability data variables 28, that may be identified andstored in the vehicle history database 30 in datasets associated with aparticular vehicle. For each insurability data variable 28 listed inFIGS. 4-9, a corresponding description 32 of that insurability datavariable 28 is also provided. However, Applicants have also realizedthat distinct inferences can be made from evaluating insurability datavariables 28 in a manner previously unknown in the prior art fordetermining insurability of vehicles. These inferences further supportApplicants' findings that for certain historical events of a particularvehicle, subsequent damage is likely to occur, and/or the severity ofthe damage and/or the cost of repair is likely to be greater. Hence,entities, such as insurance providers, may be interested in knowingwhich factors of vehicle history (such as the disclosed insurabilitydata variables 28) may affect insurability for one or more vehicles.

For example, the “Airbag Deployment” insurability data variable 28indicates that the particular vehicle's air bag has deployed. In manyinstances, it could be inferred that the particular vehicle was in anaccident. Increased amounts of fraud have been associated with servicingdeployed airbags. In one study, statistical data shows that a driver isfive percent more likely to be injured in a vehicle with a previouslydeployed airbag purported to be fixed. This may be due to failure toproperly service a previously deployed airbag. Hence a subsequentmalfunction may occur, for example, in which the allegedly repairedairbag fails to properly deploy such as in a future accident. Thus, thetendency for the driver of the vehicle to become injured increases. Inanother example, the airbag may prematurely deploy and cause injury to adriver. These events may ultimately affect insurance company premiums inorder to compensate for appropriately filed insurance claims for suchincidents. Consequently, the insurability of a vehicle that has thisinsurability data variable 28 may also be affected.

“Abandoned” insurability data variable 28 indicates that the particularvehicle was abandoned by an owner for some reason.

“Police Accident Report—Severe Damage” insurability data variable 28denotes that a police accident report indicates that the particularvehicle sustained severe damage in an accident. The “Police AccidentReport—Severe Damage” insurability data variable 28 is preferablydistinguished from a less serious accident. For example, additionalfactors usually contribute to the conclusion that an accident wassevere. This may be attributed to the kind and/or severity of damagethat the vehicle sustains such as that incurred in a vehicle that wasinvolved in a rollover accident.

“Police Accident Report” insurability data variable 28 indicates apolice accident report that does not indicate “severe” damage. In thisscenario, the vehicle may sustain considerably less damage than that ofa severe accident and may also be regarded as a less serious ornon-severe accident. A review of the “Police Accident Report”insurability data variable 28 may be important for flushing outinsurance fraud. In one example, a vehicle may incur minor damage to anarea of the vehicle which the owner declines to fix. However, thevehicle may be involved in another accident—perhaps sustainingadditional damage such as in an area proximate to the damage of thefirst accident. This may present an opportunity for the owner to engagein fraudulent activity. For example, in deciding to file an insuranceclaim for the damages of the second accident, the owner may also attemptto claim the damages for the first unrelated accident. Thus, by linkingthe two damages, the owner may purport that the accident was moreserious and caused more damaged than what really occurred. This kind offraudulent activity has also taxed insurance providers who, in turn,assess additional premiums passed on to the customer in order to coverthese additional fraudulent expenses. Additionally, some changes inprocedure have been implemented by insurance providers to address thisproblem. This may include visually inspecting vehicles prior to issuinga new insurance policy. However, this procedure can increase operatingexpenses and require additional manpower for performing such tasks. Itis therefore desirable to advise insurance providers about thisinsurability data variable 28 in a manner which would affectinsurability as disclosed herein.

“Commercial” insurability data variable 28 indicates that the particularvehicle was registered for commercial use. While this type of vehicle isgenerally well maintained, it is also typically driven very hard or in amanner that is harder than privately owned vehicles. In short, morerisks are likely taken with this kind of vehicle. Hence, the mechanicalintegrity of the vehicle is more likely to suffer or be compromised.Thus, future and/or more expensive repair costs are more likely to occurfor a vehicle determined to be commercial. Thus, knowing thispossibility and how it relates to insurability, in accordance with thedisclosed invention, it is possible to advise an entity, such as aninsurance providers accordingly.

“Curbstoning Advisory” insurability data variable 28 indicates a patternof events that suggests the vehicle was purchased from an unlicenseddealer. In one example, the particular vehicle was sold by a dealerposing as a private owner of the vehicle, such sale tactics beinggenerally employed when there is something wrong with the vehicle andthe dealer wants to deceive the buyer and not disclose the problems ofthe vehicle.

“Crash Test Vehicle” insurability data variable 28 indicates that theparticular vehicle was used in vehicle crash testing.

“Damage Disclosure” insurability data variable 28 indicates that damageto the particular vehicle was reported, for example, by the owner.

“Dismantled” insurability data variable 28 indicates that the particularvehicle contains a dismantled title brand. This indicates that theparticular vehicle was dismantled for parts or recycling, for example,by a salvage yard.

“Emissions Test Failed” insurability data variable 28 indicates that theparticular vehicle failed emissions testing in the last twenty-fourcalendar months. This insurability data variable 28 may indicate thatthe vehicle was not properly maintained or address a degree of how wellthe maintenance of the vehicle was kept. This could also be indicativeof driver behavior including, for example, that the driver tends to beirresponsible or tends not to be a good driver. Such inferences couldalso affect insurability of a vehicle.

“Fire Damage” insurability data variable 28 indicates that theparticular vehicle was damaged in a fire.

“Corporate Fleet” insurability data variable 28 indicates that theparticular vehicle was registered as part of a commercial fleet, forexample, as a company vehicle. The inferences/conclusions made for thisinsurability data variable 28 are generally the same as those listedunder “Commercial” insurability data variable 28.

“Flood” insurability data variable 28 indicates that the particularvehicle has incurred damage caused by water. This could infer that theelectrical system of this vehicle has become faulty and that theelectrical system may be more susceptible to failure in a futureoccurrence including, for example, an accident. This, at least in part,is because many components of the vehicle depend upon the electricalsystem to work properly. Additionally, there is an increased chance thatfuture repair costs will not only occur, but be more expensive.Accordingly, some entities, such as insurance providers may thus beinterested in receiving information to identify vehicles registered inknown flood areas or identifying vehicles in declared federal disasterareas to further make a decision regarding insurability.

“Gray Market Vehicle” insurability data variable 28 indicates that theparticular vehicle was originally manufactured to the standards of acountry other than the U.S.

“Hail” insurability data variable 28 indicates that the particularvehicle has incurred body damage caused by hail.

“Junk” insurability data variable 28 indicates that the particularvehicle is no longer suitable for use on public roads.

“Lease” insurability data variable 28 indicates that the particularvehicle was registered as a lease vehicle. Applicants have found thatbecause leased vehicles generally contain provisions requiring that theleased vehicle must be returned to the dealer in an undamaged condition,the owner is more likely to file an insurance claim for any damages thatmay occur. Hence, leased vehicles are generally more expensive toinsure.

“Last Owner Length of Ownership” insurability data variable 28 indicatesthe number of elapsed days since the last owner acquisition date. Thedisclosed invention determines that the longer an owner owns a vehicle,the more inclined the owner is to take care of the vehicle. Thus,insurability is more likely to increase for vehicles having long lengthsof ownership for their last owner.

“Number of Owners” insurability data variable 28 indicates that theparticular vehicle has had the indicated number of owners. Thus thenumber of owners indicates all previous or past owners. Applicants havedetermined that insurability increases for vehicles having less owners,since the value of fewer owners increases the likelihood that thevehicle has been properly serviced and/or maintained.

“Verified Rollback” insurability data variable 28 indicates that theodometer rollback was verified by a law enforcement agency. Insurancecarriers use the odometer as a barometer of reliability of the vehicle.If the vehicle odometer is “rolled back”, then the data associated withthe vehicle generally becomes unreliable. In this regard, insurabilityof a vehicle can decrease should a determination be made that a verifiedrollback occurred.

“Police” insurability data variable 28 indicates that the particularvehicle was registered for police use which suggests severe usage. Theinferences/conclusions made for this insurability data variable 28 aregenerally the same as for those listed under “Commercial” insurabilitydata variable 28.

“Personal” insurability data variable 28 indicates that the particularvehicle was registered to a private individual.

“Rental” insurability data variable 28 indicates that the particularvehicle was registered for use in a rental fleet which suggests severeusage. The inferences/conclusions made for this insurability datavariable 28 are generally the same as those listed under “Commercial”insurability data variable 28.

“Repossessed Vehicle” insurability data variable 28 indicates that theparticular vehicle was reported as being repossessed such as beingrecovered by a party including, for example, a bank that has ownershiprights to the vehicle.

“Safety Inspection Failed” insurability data variable 28 indicates thatthe particular vehicle failed safety inspection in the last twenty-fourcalendar months. The inferences/conclusions made for this insurabilitydata variable 28 are generally the same as those listed under “EmissionsTest Failed” insurability data variable 28.

“Taxi” insurability data variable 28 indicates that the particularvehicle was registered for use as a taxi. The inferences/conclusionsmade for this insurability data variable 28 are generally the same asthose listed under “Commercial” insurability data variable 28.

“Theft Recovery” insurability data variable 28 indicates that theparticular vehicle was stolen, but then was recovered. Inferencesderived from this insurability data variable 28 may include evidencewhich supports that stolen vehicles are typically more abused or drivenharder in a manner in which the vehicle was never intended to be driven.(This may include, for example, being used in a robbery such as a“get-away” vehicle.) Accordingly, the integrity of the vehicle may becompromised including various components related to, for example, thebrakes, the frame, the steering, and the body. Therefore, even though astolen vehicle has been recovered, the disclosed invention asserts thatinsurers may be advised of insuring such vehicles due to the likelihoodof subsequent and costly damage in accordance with the presentinvention.

“Insurance Total Loss” insurability data variable 28 indicates that theparticular vehicle was in an accident and that the total value of thevehicle was paid to the insured by the insurance company that insuredthe vehicle, rather than paying for the repair of the vehicle. Thus, theinsurance company took ownership of the particular vehicle due to atotal loss payment.

Whereas many of the above noted insurability data variables 28 wouldgenerally reduce the insurability of the used vehicle, some of theinsurability data variables 28 may increase the insurability of thevehicle as well, depending on the selection of insurability datavariables 28 under consideration. For example, the likelihood ofinsurability of a used vehicle may be increased if the number ofprevious owners is significantly lower than expected for the age of thevehicle. Of course, the converse may be true in that the likelihood ofinsurability of a used vehicle may be reduced if the vehicle has asignificantly higher number of previous owners than expected for the ageof the vehicle.

The insurability data variables 28 may be retrieved, processed,displayed, and/or imported/exported to other databases or forwarded toother entities. For example, the insurability data variables 28retrieved and processed by the vehicle history report module 35 tocreate corresponding vehicle history reports for a particular VIN can bedisplayed by the user interface module 42. Thus, one aspect of theinvention includes utilizing at least one insurability data variable 28for determining insurability of a vehicle. Another aspect of the presentinvention includes utilizing a predetermined group of insurability datavariables 28 for determining insurability of a vehicle as discussed morefully herein below. Moreover, once an insurance policy has beenunderwritten and rated, the unique set of data variables 28 may also beutilized to determine a premium for a particular vehicle. It should benoted that some insurability data variables 28 can be obtained directlyfrom existing records, such as vehicle title records. However, someinsurability data variables must be derived by processing informationfrom existing vehicle records.

As discussed earlier, the preferred embodiment recognizes that certainvehicle history attributes, or insurability data variables 28, correlateto future repair costs of a vehicle. The likelihood and amount of repaircosts of a vehicle may impact the insurability of that vehicle. Thepresent invention utilizes a statistical comparison of insurancepremiums and insurance claim values for two different values of aparticular vehicle attribute as one way of determining whether thevehicle history attribute affects future repair costs and thusinsurability, and therefore whether the attribute is an insurabilitydata variable 28.

According to one disclosed embodiment the aforementioned statisticalcomparison is defined as a loss ratio. For example, the calculated lossratio, as determined by an entity such as an insurance company, alsoinfluences the selection of certain data variables 28 within a datagrouping 34 (as discussed further below). The disclosed embodimentincludes evaluating data, such as from insurance companies, to determinea percentage of dollar loss value for the entity before and after aspecific event related to a vehicle. For example, in reviewing a seriesof vehicles in which the airbag was deployed, Applicants determined thatthe vehicle was more likely to have a subsequent problem related to theairbag in a future unrelated incident such as an accident. This may bebecause of a failure to properly fix the originally deployed airbag.Thus there may be another malfunction of the airbag such as prematuredeployment or failure of deployment.

In another example, such as relating to frame damage, the degradation toan original frame of a vehicle may be such that even if it wereallegedly fixed from a first accident, the vehicle still may have atendency to be more susceptible to crumple at the originally damagedlocation of the frame in a subsequent accident.

Thus, when evaluating the loss ratio, the present invention includesevaluating the ratio of insurance claims to insurance premiumscollected. Applicants have determined that the dollar loss value isdifferent for insured vehicles after the occurrence of a specific event(such as an airbag deployment or damage to a frame) than for vehicles inwhich the aforementioned specific event never occurred. This difference,or loss ratio, can be calculated for a multitude of vehicle historyattributes to determine whether a correlation to future repair costsexists.

One example of a loss ratio analysis is as follows:

# of % of Loss LR Airbag Deployed VINs VINs Premium $ Loss $ RatioRelativity No 194,908  99.74% $231,767,626 $122,216,470 52.73% 1.00 Yes503  0.26%    $671,372    $486,320 72.44% 1.37 Total 195,411 100.00%$232,438,998

The loss ratio for those vehicles in which the airbag was deployed issignificantly greater than the loss ratio for those vehicles without anairbag deployment. Therefore, it may be concluded that the fact thatcertain vehicles have past airbag deployment increases the likelihoodand/or amount of future insurance claims/repair costs for these samevehicles. Consequently, the value of the airbag attribute for anyparticular vehicle is an important factor in determining insurabilityand therefore is classified as an insurability data variable 28.

The following table is provided as another example for a loss ratioanalysis:

# of % of Loss LR Frame Damage VINs VINs Premium $ Loss $ RatioRelativity No 194,482  99.52% $231,239,975 $121,593,633 52.58% 1.00 Yes929  0.48%  $1,199,023  $1,109,157 92.51% 1.76 Total 195,411 100.00%$232,438,998

The loss ratio for those vehicles in which frame damage occurred issignificantly greater than the loss ratio for those vehicles withoutframe damage. Therefore, it may be concluded that the fact that certainvehicles have past frame damage increases the likelihood and/or amountof future insurance claims/repair costs for these same vehicles.Consequently, the value of the frame damage attribute for any particularvehicle is an important factor in determining insurability and thereforeis classified as an insurability data variable 28.

Another aspect of the preferred embodiment involves the groupingtogether of similar insurability data variables 28 that are, forexample, similar in type, and/or similar in loss ratio values. By way ofexample, certain insurability data variables 28 from database 30 havebeen selected and categorized or grouped into the data groupings 34shown in FIGS. 4-9. Thus, in one disclosed embodiment, selectedinsurability data variables 28 having similar loss ratios are placedwithin certain data groupings 34. The selection or grouping based onloss ratios may also take into consideration the type of insurabilitydata variables 28 as outlined above. For example, all variables having asignificant damage event may be grouped together to form a SevereProblem File 68. Also, all variables relating to mileage may be groupedtogether to form an Annual Mileage File 76.

Upon comparison of similar types of insurability data variables 28 andloss ratios, for example, as outlined above, the disclosed embodimentcomprises at least six data groupings 34 for determining insurability asdisclosed herein. As shown, for example, in FIGS. 4-9, the datagroupings 34 are classified as a Severe Problem File 68, a PotentialDamage File 70, an Ownership History File 72, an Ownership Type File 74,an Annual Mileage File 76, and a Potential Fraud File 78. Theaforementioned data groupings 34 are provided as one exemplaryembodiment and should not be understood to limit the invention. Thus,additional data groupings 34 comprising respective insurability datavariables 28, in accordance with aspects of the present invention, mayalso be created. These data groupings 34 may be utilized in accordancewith disclosed embodiments, for example, to facilitate a determinationof insurability as disclosed herein.

In the preferred embodiment shown, a vehicle history data analysis unit26 includes appropriate hardware and software for implementing thevehicle history report module 35, the data determination module 36, andthe user interface module 42, each module performing the functions asdescribed in detail below. In this regard, vehicle history data analysisunit 26 may be implemented as a general purpose computing device with acentral processing unit (CPU) or processor. The software for operatingthe vehicle history data analysis unit 26 and the various modules mayreside in a computer readable storage medium in the form of executableinstructions that operate the vehicle history information system 12 andperform the functionalities and process steps described.

In particular, the vehicle history report module 35 functions to accessvehicle history database 30 to retrieve appropriate vehicle historyrecords associated, for example, with a particular VA that is requestedby a user of the vehicle history information system 12. Thus, thevehicle history module 35 includes the appropriate software necessary toidentify the appropriate vehicle history dataset from the vehiclehistory database 30, and to retrieve vehicle history data based on aparticular request for example, a query limited to a particular. VIN orplural VINs. The vehicle history report module 35 may further be adaptedto arrange and organize the vehicle history data and information in amanner appropriate for further data processing and/or display as avehicle history report via the user interface module 42 described below.

User interface module 42 is adapted to generate a user interface oroutput for delivery to output device 24 of customer terminal 14. Inparticular, the user interface module 42 may be adapted to generateparticular electronically displayable files for delivery to, and displayby, output device 24 of customer terminal 14. For example, the userinterface module 42 may utilize the information provided by the vehiclehistory report module 35 described in further detail below to generatean output which is provided to the output device 24 of terminal 14.Communications managing module 33 is adapted to manage communicationsand interactions between vehicle history information system 12 and itsvarious components, as well as with the various terminals 14 via thedistributed network 16.

The vehicle data determination module 36 of the vehicle history dataanalysis unit 26 is adapted to provide a value of one or moreinsurability data variables 28 as listed, for example, in FIGS. 4-9. Thevalues for each insurability data variable 28 are preferably indicatorsthat the particular vehicle possesses or does not possess thecharacteristic of the particular insurability data variable 28. Forexample, if the vehicle history information indicates that the vehiclehas or is likely to have a particular insurability data variable 28,such as frame damage, then the value for the “frame damage” insurabilitydata variable 28 is a positive indication, for example, a “yes.” On theother hand, if the insurability data variable 28 does not indicate thatthe vehicle has or is likely to have a particular insurability datavariable 28, such as having no frame damage, then the value for the“frame damage” insurability data variable 28 is a negative indication,for example, a “no.” Thus, for a particular insurability data variable28, a value of the aforementioned data variable 28 is affirmed (e.g.,via a “yes” indication) or denied (e.g., via a “no” indication”), andunderwriting and/or rating may be based on the affirmation or denial ofthe value of the insurability data variable 28.

In other instances, the resulting information provided by evaluating adata variable 28 may not necessarily provide an indicator such as apositive or negative indicator, for example “yes” or “no,” respectively.For example, the “Ownership History File” 72 of FIG. 6 includes twoinsurability data variables 28, “Number of Owners” and “Last OwnerLength of Ownership”, which will provide a numerical indicator upon anevaluation thereof. In this example, the aforementioned data variables28 are associated with a number which may be evaluated and/or analyzedagainst a predetermined requirement. For example, the question may bewhether the numerical indicator of the data variable 28 exceeds athreshold value or falls within a particular numerical range.

In keeping with the above detailed description of “Number of Owners”,Applicants have determined that insurability increases for vehicleshaving less owners, since the value of fewer owners is more likely toreflect a vehicle that has been properly serviced and/or maintained.Also, in accordance with the above description of “Last Owner Length ofOwnership”, the disclosed invention determines that the longer an ownerowns a vehicle, the more inclined the owner is to take good care of thevehicle. Thus, insurability is more likely to increase for vehicleshaving long lengths of ownership for their last owner. In the currentexamples, “Number of Owners” may equal five, and “Last Owner Length ofOwnership” may equal four months. According to the invention, thedecision to underwrite may be declined if the number of owners is morethan two. Also, the decision to underwrite may be declined if the lastowner length of ownership is less than twelve months. In addition, forexample, the rating of an insurance policy may reflect a higher risk anda higher premium if the number of owners is more than two or if the lastowner length of ownership is less than twelve months.

For certain data groupings 34, the vehicle data determination module 36of the vehicle history data analysis unit 26 may be adapted to providean overall value of an entire data groupings 34. That is, if any oneinsurability data variable 28 of a particular data grouping 34 has apositive indication, for example, a “yes,” then the overall value of thedata grouping 34 is also positive or “yes.” For example, in reviewingthe “Severe Problem File” 68 of FIG. 4, the “Severe Problem Flag”indicates that for any type of title brand, severe accident indicator,or stolen vehicle indicator listed in the data grouping 34, an overallvalue of the data grouping 34 may be established by a positiveindication, for example, a “yes,”. Thus, in this example, if the overallvalue is “yes,” then the determination is made not to offer an insurancepolicy to an applicant. Alternatively, if the overall value is “no,”then the determination is made to offer an insurance policy to theapplicant to assign the applicant to a particular policy class, and torate the policy. The overall value may also be used to rate the policysuch that, for example, a “no” would still permit a policy to be writtenbut require a rating reflecting higher risk, e.g. a higher premium. Inaddition, a number of data groupings 34, for example, three or more, maybe selected and evaluated as a basis for underwriting and rating aninsurance policy. Thus, underwriting and rating may be based on acombination of respective overall values of the data groupings 34.Hence, if the majority or all of the overall values of the datagroupings 34 are in the affirmative, e.g., “yes,” or negative, e.g.,“no,” then underwriting and rating are appropriately made based on thisresult.

Thus, the information, i.e., overall value provided by an analysis ofthe insurability data groupings 34, can facilitate a determination ofinsurability as disclosed herein. This, at least, is due to thesimilarity of the insurability data variables 28 listed in the datagrouping 34 in combination with the related loss ratios of the listedinsurability data variables 28. The thusly formed data groupings 34,created in accordance with the disclosed invention, can provideinformation previously unknown in the art for determining insurability,since the data grouping 34 is specifically packaged to provide specificvehicle history information regarding future losses. In a practicalsense, underwriting an insurance policy based on an overall value of theone or more data groupings 34 is indicative of the likelihood ofincurring future vehicle repair costs.

While the disclosure describes use of the data groupings 34 comprised ofinsurability data variables 28, it is noted that the insurability datavariables 28 have separate utility apart from being grouped into theaforementioned data groupings 34. The value of the insurability datavariables 28 can be reviewed, for example, by an insurance company (orother entity), and used as a basis for underwriting and/or rating aninsurance policy as described herein. This may include a scenario inwhich the value of one insurability data variable 28 is evaluated andunderwriting and rating occurs based on this value. In another scenario,the values of a plurality of insurability data variables 28, whethersimilar or not, may be evaluated and underwriting and rating occursbased upon the results, e.g., any one value exists, a majority of thevalues exist, or a combination of the values exist or do not exist.

Thus, in one example, as shown for instance, in FIG. 10, one or morevehicle identification numbers (VINS) may be submitted (step 38) uponwhich insurability data variables 28 (step 40) are respectivelyassociated with the one or more submitted VINS. Each insurability datavariable 28 will indicate a value of the vehicle based on whether thatparticular vehicle possesses or does not possess the characteristic ofthe particular insurability data variable 28. The insurability datavariables 28 may further be combined into preselected data groupings 34as previously discussed. Based upon a value of the respectiveinsurability data variables 28 or the overall value of the respectivedata groupings 34, underwriting of insurance policy (step 44) occursbased on the values of the insurability data variables 28 or the overallvalues of the one or more data groupings 34. Furthermore, if aninsurance company decides to offer an insurance policy to the applicant,the policy may be rated (step 46) based upon information associated withthe insurability data variables 28, such as the value of one or morevariables, the overall value of one or more groups of variables, or avalue or a score based on one or more data variables as discussed hereinbelow. In another embodiment, the information associated with theinsurability data variables 28 may be used only for rating.

Turning to FIG. 11, an example of an insurance evaluation and ratingtransaction 50 according to one disclosed embodiment is illustrated. Aninsurance company may be granted access to the vehicle historyinformation (step 52) provided, for example, by a vehicle historydatabase supplier. In one embodiment, the vehicle history databasesupplier maintains the vehicle history information system 12 (FIGS. 1and 2) and is capable of providing data variables 28, and/or groupingdata variables 28 to create data groupings 34, according to preferencesof the insurance company. The insurance company determines and requestswhich data groupings 34 will be provided from the vehicle historydatabase supplier for an agreed transaction price. In one example, theaforementioned transaction price may be based upon a number of VINnumbers submitted to the vehicle history database supplier by theinsurance company.

Thus, the insurance company submits the VINS (step 54), for example,individually or in a batch submission, to the vehicle history databasesupplier. The vehicle history database supplier may have an establisheddistributed network 16 (FIGS. 1 and 2) in place to receive and forwardthe VINS submitted by the insurance company to the vehicle historyinformation system 12 (FIGS. 1 and 2). Alternatively, vehicle historydatabase supplier may allow the insurance company to submit the VINSdirectly to the vehicle history information system 12.

The VINS are validated (step 56) and the process continues. In oneembodiment, the validation process may include receiving information toconfirm the vehicle of interest. For example, FIG. 3 depicts a“Demographic File” 66 which provides an exemplary array of informationor vehicle identification data 29. The Demographic File 66 may behelpful in confirming that the requested information for the submittedone or more VINS is indeed attributed to the correct one or morevehicles. Thus, the description 37 of each vehicle identification data29 may further facilitate confirmation of one or more preferredvehicles. In an instance wherein the VINS are not confirmed, the VINSmay be checked for accuracy and resubmitted for processing (step 48).Alternatively, the process may end (step 60).

Upon confirmation of the VINS (step 56), insurability data variables 28are collected from a larger set of vehicle attributes in vehicledatabase 30 associated with each vehicle and forwarded to the insurancecompany (step 58). In the present example, the insurability datavariables 28 are gathered into the data groupings 34. Upon receiving thedata variables 28 and data groupings 34, the insurance company proceedswith the underwriting process including determining whether to offer aninsurance policy based on the received information (step 62). In someinstances, the insurance company may deny insurance policy requests(step 60), for example, when any one, or certain combination of datavariables has a positive value, or any one, or any combination of datagroupings has a positive value. In other cases, the insurance companymay grant, i.e. decide to write, an insurance policy, for example, whenno values, or overall values are positive. In addition, the insurancecompany may rate the granted insurance policy based on the received datavariable 28 and data grouping 34 information (step 64).

In a particular embodiment, an analysis of insurability data variables28 selected, for example, from the vehicle history database 30 may beperformed in which the insurability data variables 28 are utilized togenerate a scoring result, i.e., a score. Thus, the score may be used tounderwrite an insurance policy. Importantly, the score may alsofacilitate rating an insurance policy to determine an insurance price orpremium. The score may include a numerical value, for example,indicative of a value for an insurance price or premium for a potentialcustomer. That is, the score may be used to generate an overallinsurance score attributed to an assessment of risk for a prospectiveinsurance consumer. The underwriting and scoring decisions may be basedon the same criteria or rules or may be based on separate criteria orrules.

Thus, in one example, the insurance company supplies one or more VINS tothe vehicle history database supplier. Insurability data variables 28are generated, for example, in accordance with a predeterminedagreement. One or more of the insurability data variables 28 may be fedinto an algorithm for generating an output. The output may becharacterized as a score, which may include a numerical value, mark,symbol, color, and/or any other suitable representation for generatingan output. Hence, in one embodiment, the score may include a singlenumerical value indicative of a result of the data variables 28 beingfed within and processed by the algorithm. Furthermore, each of therespective data variables 28 fed to the algorithm may be suitablyweighted according to a user preference. Thus, in one embodiment,certain insurability data variables 28 may be weighted more than others,because a user may be interested in particular trends or characteristicsof some data variables 28 more than others. For example, an entity suchas an insurance company or user, may prefer that certain insurabilitydata variables 28 have a greater impact on insurability than otherinsurability data variables 28. Therefore, in this embodiment,underwriting, that is, determining whether to offer an insurance policyand assigning the policy to a particular policy class based upon therisk presented by the variables, and rating the policy, is not based onany one value or any one overall value but a weighted combination, i.e.,algorithmic or mathematical model or equation.

The algorithm may be executed by a software program appropriatelyconfigured to run the algorithm and produce the output describedhereinabove. The algorithm may be adaptable for receiving inputted datasuch as the insurability data variables 28. Thus, in one embodiment,data, such as insurability data variables 28, may be inputted via thesoftware program into the algorithm whereupon the software programexecutes the algorithm to produce an output such as the disclosed score.In operation, an entity, such as the vehicle history database supplier,may provide a service to a client, such as an insurance company. Theservice may include running the algorithm and providing a score to theinsurance company. Alternatively, the algorithm may be run directly by aparty desiring an output of the algorithm (e.g., the score as disclosedherein).

Hence, in accordance with one embodiment, an algorithm may be providedas follows:

[X]A+[Y]B . . . +[Z]C=SCORE

wherein X, Y, and Z are weight factors and A, B, and C are datavariables 28 selected from the vehicle history database 30. Thus, in oneexemplary application, the following formula based on the disclosedalgorithm model is provided as:

(A*125)+(B*50)+(C*20)=SCORE

where A is the value of the title brand data variable, B is the value ofthe frame damage data variable, and C is the value of the severeaccident data variable; and wherein,

If a title brand exists, then A=1

If no title brand exists, then A=0

If a frame damage record exists, then B=1

If no frame damage record exists, then B=0

If a severe accident record exists, then C=1

If no severe accident record exists, then C=0

In this example, the value of insurability data variable A, directed totitle brand, is weighted more heavily than the value of insurabilitydata variables B and C, directed to frame damage and severe accident,respectively. Likewise, the value of insurability data variable B isweighted more heavily than the value of insurability data variable C.The higher weighted insurability data variable is, therefore, given ahigher impact on insurability than a lower weighted insurability datavariable. Of course, in an embodiment using data groupings 34, A, B, andC (in the above example) may represent overall values of data groupings34.

In the previous example, the score is represented as a numerical value.Thus, underwriting of a prospective insurance policy may rely on whetherthe score falls into a numerical range. For example, if the availablescoring range is from 0-100, then it may be determined that scores from70-100 are acceptable and an insurance policy should be offered and thusa score below 70 will indicate that no policy should be offered to theapplicant. Importantly, furthermore, the same numerical result of thescore may be utilized to determine rating of the insurance policy inorder to set a price or premium. For example, if the score falls in arange of 70-80, then the price or premium is set at a first dollaramount. Similarly, if the score falls in a range of 80-90, then theprice or premium is set at a second dollar amount different than thefirst dollar amount. Likewise, if the score falls in a range of 90-100,then the price or premium is set at a third dollar amount different thanthe first and second dollar amounts. Alternatively, the rating procedurecan be based on a separate algorithm or rule and/or a separate score,with respect to the underwriting procedures.

As discussed above, the output i.e., score, of the algorithm may beuseful in a variety of applications for underwriting and rating aninsurance policy. For example, if the score is represented as a color,such as green, or a mark such as “+”, or a word such as “go”, then theindication of green, or “+” or “go” means that it is advisable to offerand assign the respective insurance policy. Alternatively, if the scoreis represented as a color, such as red, or a mark such as “+”, or a wordsuch as “stop”, then the indication of red, or “−”, or “stop” means thatit is not advisable to offer the respective insurance policy.

Also, the score may be used by to determine an insurance price orpremium. For example, having calculated a score in accordance with thealgorithm above, wherein the score is a numerical value, a calculatedinsurance premium (IP) may be determined as follows:

IP=P*SCORE/100

wherein P is a computed insurance price or premium based uponnon-vehicle history data variables. The variable “P” may be based uponcriteria utilized in typical calculations for determining an insuranceprice or premium, such as the driving record or history of theparticular person/driver listed on the policy. Thus, the value “IP”,according to the disclosed embodiment, provides a modified or “new”price value which otherwise generates an updated insurance price orpremium based upon the disclosed score.

Hence, a practical embodiment of the use of the algorithm incorporatinginsurability data variables 28 has been shown. It has been further shownthat the insurability data variables 28 may also be manipulated, such asbeing weighted against one or more insurability data variables 28 withinthe algorithm, in order to affect insurability as deemed appropriate.

Thus the embodiment not only provides a method and system forunderwriting and rating an insurance policy, but also a method andsystem for generating information, i.e. overall value of one or moregroups of variables, values of individual data variables, a score, etc,useful in underwriting and rating insurance policies. That is, theinsurance company may receive the value of one or more variables, theoverall value of one or more groups and/or a score or value, and thenuse that data to underwrite and/or rate by further processing the data,such as described herein.

Any algorithm or rule can be applied to vehicle history data variables,individually or as a group, to determine values to be used forunderwriting and/or rating. The values can be expressed as scores,binary values, numeric or non numeric variables, or the like. Theinvention can be used for underwriting, rating, or both processes.Underwriting and rating can be accomplished as shown in FIG. 10,separately, or in parallel.

While various embodiments in accordance with the present invention havebeen shown and described, it is understood that the invention is notlimited thereto. The present invention may be changed, modified andfurther applied by those skilled in the art. Therefore, this inventionis not limited to the detail shown and described previously, but alsoincludes all such changes and modifications.

1. A method of underwriting an insurance policy comprising: identifyinga vehicle; forming at least one data grouping including at least onevehicle history data variable of the identified vehicle; processing theat least one vehicle history data variable of the at least one datagrouping; determining an overall value of said at least one datagrouping; and underwriting an insurance policy for the identifiedvehicle based on said overall value of the at least one data grouping.2. The method of claim 1, further comprising: rating the insurancepolicy for the identified vehicle based on said overall value of said atleast one data grouping.
 3. The method of claim 2, wherein said at leastone data grouping includes a plurality of data groupings, said methodfurther comprising generating a score based upon an overall value ofeach of the plurality of data groupings and rating the policy based onthe score.
 4. The method of claim 3, wherein said score is calculatedbased on a weighted combination of overall values of said plurality ofdata groupings.
 5. The method of claim 4, wherein said score is the sumof weighted overall values of each of said plurality of data groupingsrepresented as a mathematical equation.
 6. The method of claim 1,wherein the step of determining an overall value is based on whether avehicle possesses or does not possess a characteristic of at least someof said at least one vehicle history data variable.
 7. The method ofclaim 1, wherein said at least one data grouping includes a plurality ofdata groupings.
 8. The method of claim 1, wherein vehicle history datavariables formed within a data grouping are similar.
 9. The method ofclaim 8, wherein the similarity of the plurality of vehicle history datavariables within a data grouping includes being similar in at least oneof type and loss ratio value.
 10. The method of claim 1, wherein the atleast one data grouping includes at least one of: a Severe Problem datagrouping; a Potential Damage data grouping; a Ownership History datagrouping; a Ownership Type data grouping; a Annual Mileage datagrouping; and a Potential Fraud data grouping.
 11. The method of claim1, wherein the overall value is indicative of the likelihood ofincurring future vehicle repair costs.
 12. The method of claim 7,further comprising: generating a score based upon the overall value ofeach of the plurality of data groupings; and wherein said underwritingstep comprises underwriting an insurance policy for the selected vehiclebased on the score.
 13. The method of claim 12, wherein the score ischaracterized as at least one of a number, mark, symbol, and color. 14.The method of claim 12, wherein said score is calculated based on aweighted combination of overall values of said plurality of datagroupings.
 15. The method of claim 14, wherein said score is the sum ofweighted overall values of each of said plurality of data groupingsrepresented as a mathematical equation.
 16. The method of claim 1,wherein the vehicle history data variables include at least one of: AirBag Deployed; Abandoned; Canadian Total Loss; Commercial Vehicle;Corporate Fleet Crash Test Vehicle; Curbstoning Advisory; DamageDisclosure; Dismantled; Duplicate Title Issued; Emissions Test;Estimated Current Mileage; Exceeds Mechanical Limits; Exported Vehicle;Flood Advisory; Flood Damaged; Fire; Fire Damage; Frame Inspection; GreyMarket Vehicle; Gross Polluter; Hail Damaged; Inconsistent OdometerReading; Imported Vehicle; Junk; Last Owner—First Odometer Reading; LastOwner—First Odometer Reading Date; Last Owner—Last Odometer Reading;Last Owner—Last Odometer Reading Date; Last Owner—Average AnnualMileage; Last Owner—Recent Annual Mileage; Last Owner—Last 12 MonthsAverage Annual Mileage; Last Owner—Last 24 Months Average AnnualMileage; Last Owner—Last 36 Months Average Annual Mileage; Last OwnerLength of Ownership; Last Owner Lien Reported; Lease Vehicle;Manufacturer Buyback (Lemon); Not Actual Mileage; Number of Owners;Number of Owners Footnote; Odometer Problem Flag; Odometer Rollover;Personal; Police Accident Report; Police Accident Report—Severe Damage;Police Vehicle; Potential Damage Flag; Potential Odometer Problem;Rebuilt Title Brand; Reconstructed Title Brand; Recycle Vehicle; RentalVehicle; Reported as Stolen; Repossessed; Safety Inspection Failed;Salvaged Auction; Severe Accident Flag; Stolen Vehicle Flag; ReportedStolen; Theft Recovery; Insurance Total Loss; Taxi Vehicle; VerifiedOdometer Rollback; and VIN Cloning Advisory.
 17. The method of claim 1,wherein said forming step comprises deriving a vehicle history datavariable for at least one of: Number of Owners; Duplicate Title Issued;Estimated Current Mileage; Flood Advisory; Gray Market Vehicle LastOwner—Annual Mileage; Last Owner—Recent Annual Mileage; LastOwner—Length of Ownership; Potential Damage Flag; Severe Accident Flag;and Insurance Total Loss.
 18. A method of rating an insurance policycomprising: identifying a vehicle; forming at least one data groupingincluding of least one vehicle history data variable of the identifiedvehicle; processing the at least one vehicle history data variable ofthe at least one data grouping; determining an overall value of said atleast one data grouping; and rating an insurance policy for theidentified vehicle based on said overall value of the at least one datagrouping.
 19. The method of claim 18, wherein the step of determining anoverall value is based on whether a vehicle possesses or does notpossess a characteristic at least some of said at least one vehiclehistory data variable.
 20. The method of claim 18, wherein said at leastone data grouping includes a plurality of data groupings.
 21. The methodof claim 18, wherein vehicle history data variables formed within a datagrouping are similar.
 22. The method of claim 21, wherein the similarityof vehicle history data variables within a data grouping includes beingsimilar in at least one of type and loss ratio value.
 23. The method ofclaim 18, wherein the at least one data grouping includes one of: aSevere Problem data grouping; a Potential Damage data grouping; aOwnership History data grouping; a Ownership Type data grouping; aAnnual Mileage data grouping; and a Potential Fraud data grouping. 24.The method of claim 18, wherein the overall value is indicative of thelikelihood of incurring future vehicle repair costs.
 25. The method ofclaim 20, further comprising: generating a score based upon an overallvalue of each of the plurality of the data groupings; and wherein saidrating step comprises rating the policy based on the score.
 26. Themethod of claim 25, wherein the score is characterized as one of anumber, mark, symbol, and color.
 27. The method of claim 25, whereinsaid score is calculated based on a weighted combination of overallvalues of said plurality of data groupings.
 28. The method of claim 27,wherein said score is the sum of weighted overall values of each of saidplurality of data groupings represented as a mathematical equation. 29.The method of claim 18, wherein the vehicle history data variablesinclude at least one of: Air Bag Deployed; Abandoned; Canadian TotalLoss; Commercial Vehicle; Corporate Fleet Crash Test Vehicle;Curbstoning Advisory; Damage Disclosure; Dismantled; Duplicate TitleIssued; Emissions Test; Estimated Current Mileage; Exceeds MechanicalLimits; Exported Vehicle; Flood Advisory; Flood Damaged; Fire; FireDamage; Frame Inspection; Grey Market Vehicle; Gross Polluter; HailDamaged; Inconsistent Odometer Reading; Imported Vehicle; Junk; LastOwner—First Odometer Reading; Last Owner—First Odometer Reading Date;Last Owner—Last Odometer Reading; Last Owner—Last Odometer Reading Date;Last Owner—Average Annual Mileage; Last Owner—Recent Annual Mileage;Last Owner—Last 12 Months Average Annual Mileage; Last Owner—Last 24Months Average Annual Mileage; Last Owner—Last 36 Months Average AnnualMileage; Last Owner Length of Ownership; Last Owner Lien Reported; LeaseVehicle; Manufacturer Buyback (Lemon); Not Actual Mileage; Number ofOwners; Number of Owners Footnote; Odometer Problem Flag; OdometerRollover; Personal; Police Accident Report; Police AccidentReport—Severe Damage; Police Vehicle; Potential Damage Flag; PotentialOdometer Problem; Rebuilt Title Brand; Reconstructed Title Brand;Recycle Vehicle; Rental Vehicle; Reported as Stolen; Repossessed; SafetyInspection Failed; Salvaged Auction; Severe Accident Flag; StolenVehicle Flag; Reported Stolen; Theft Recovery; Insurance Total Loss;Taxi Vehicle; Verified Odometer Rollback; and VIN Cloning Advisory. 30.The method of claim 18, wherein said forming step comprises deriving avehicle history data variable for at least one of: Number of Owners;Duplicate Title Issued; Estimated Current Mileage; Flood Advisory; GrayMarket Vehicle Last Owner Annual Mileage; Last Owner—Recent AnnualMileage; Last Owner—Length of Ownership; Potential Damage Flag; SevereAccident Flag; and Insurance Total Loss.
 31. A method of determininginsurability of a vehicle comprising: identifying a vehicle; forming atleast one data grouping including at least one vehicle history datavariable of the identified vehicle; processing the at least one vehiclehistory data variable of the at least one data grouping; determining anoverall value of said at least one data grouping; and calculatinginsurability of the identified vehicle based on said overall value ofthe at least one data grouping.
 32. The method of claim 31, wherein saidcalculating step comprises rating an insurance policy for the identifiedvehicle based on said overall value of said at least one data grouping.33. The method of claim 31, wherein said calculating step comprisesunderwriting an insurance policy for the identified vehicle based onsaid overall value of said at least one data grouping.
 34. The method ofclaim 31, wherein said at least one data grouping includes a pluralityof data groupings, said method further comprising generating a scorebased upon an overall value of each of the plurality of data groupingsand calculating insurability of the identified vehicle based on thescore.
 35. The method of claim 34, wherein said score is calculatedbased on a weighted combination of overall values of said plurality ofdata groupings.
 36. The method of claim 35, wherein said score is thesum of weighted overall values of each of said plurality of datagroupings represented as a mathematical equation.
 37. The method ofclaim 31, wherein the step of determining an overall value is based onwhether a vehicle possesses or does not possess a characteristic of atleast some of said at least one vehicle history data variable.
 38. Themethod of claim 31, wherein said at least one data grouping includes aplurality of data groupings.
 39. The method of claim 31, wherein vehiclehistory data variables formed within a data grouping are similar. 40.The method of claim 39, wherein the similarity of the plurality ofvehicle history data variables within a data grouping includes beingsimilar in at least one of type and loss ratio value.
 41. The method ofclaim 31 wherein the at least one data grouping includes at least oneof: a Severe Problem data grouping; a Potential Damage data grouping; aOwnership History data grouping; a Ownership Type data grouping; aAnnual Mileage data grouping; and a Potential Fraud data grouping. 42.The method of claim 31, wherein the overall value is indicative of thelikelihood of incurring future vehicle repair costs.
 43. The method ofclaim 34, wherein the score is characterized as at least one of one of anumber, mark, symbol, and color.
 44. The method of claim 34, whereinsaid score is calculated based on a weighted combination of overallvalues of said plurality of data groupings.
 45. The method of claim 44,wherein said score is the sum of weighted overall values of each of saidplurality of data groupings represented as a mathematical equation. 46.The method of claim 31, wherein the vehicle history data variablesinclude at least one of: Air Bag Deployed; Abandoned; Canadian TotalLoss; Commercial Vehicle; Corporate Fleet Crash Test Vehicle;Curbstoning Advisory; Damage Disclosure; Dismantled; Duplicate TitleIssued; Emissions Test; Estimated Current Mileage; Exceeds MechanicalLimits; Exported Vehicle; Flood Advisory; Flood Damaged; Fire; FireDamage; Frame Inspection; Grey Market Vehicle; Gross Polluter; HailDamaged; Inconsistent Odometer Reading; Imported Vehicle; Junk; LastOwner—First Odometer Reading; Last Owner—First Odometer Reading Date;Last Owner—Last Odometer Reading; Last Owner—Last Odometer Reading Date;Last Owner—Average Annual Mileage; Last Owner—Recent Annual Mileage;Last Owner—Last 12 Months Average Annual Mileage; Last Owner—Last 24Months Average Annual Mileage; Last Owner—Last 36 Months Average AnnualMileage; Last Owner Length of Ownership; Last Owner Lien Reported; LeaseVehicle; Manufacturer Buyback (Lemon); Not Actual Mileage; Number ofOwners; Number of Owners Footnote; Odometer Problem Flag; OdometerRollover; Personal; Police Accident Report; Police AccidentReport—Severe Damage; Police Vehicle; Potential Damage Flag; PotentialOdometer Problem; Rebuilt Title Brand; Reconstructed Title Brand;Recycle Vehicle; Rental Vehicle; Reported as Stolen; Repossessed; SafetyInspection Failed; Salvaged Auction; Severe Accident Flag; StolenVehicle Flag; Reported Stolen; Theft Recovery; Insurance Total Loss;Taxi Vehicle; Verified Odometer Rollback; and VIN Cloning Advisory. 47.The method of claim 31, wherein said forming step comprises deriving avehicle history data variable for at least one of: Number of Owners;Duplicate Title Issued; Estimated Current Mileage; Flood Advisory; GrayMarket Vehicle Last Owner—Annual Mileage; Last Owner—Recent AnnualMileage; Last Owner—Length of Ownership; Potential Damage Flag; SevereAccident Flag; and Insurance Total Loss.
 48. An apparatus forunderwriting an insurance policy comprising: means for identifying avehicle; means for forming at least one data grouping including at leastone vehicle history data variable of the identified vehicle; means forprocessing the at least one vehicle history data variable of the atleast one data grouping; means for determining an overall value of saidat least one data grouping; and means for underwriting an insurancepolicy for the identified vehicle based on said overall value of the atleast one data grouping.
 49. The apparatus of claim 48, furthercomprising: means for rating the insurance policy for the identifiedvehicle based on said overall value of said at least one data grouping.50. The apparatus of claim 49, wherein said at least one data groupingincludes a plurality of data groupings, said apparatus furthercomprising means for generating a score based upon an overall value ofeach of the plurality of data groupings and rating the policy based onthe score.
 51. The apparatus of claim 50, wherein said score iscalculated based on a weighted combination of overall values of saidplurality of data groupings.
 52. The apparatus of claim 51, wherein saidscore is the sum of weighted overall values of each of said plurality ofdata groupings represented as a mathematical equation.
 53. The apparatusof claim 48, wherein the means for determining an overall valuedetermines the overall value based on whether a vehicle possesses ordoes not possess a characteristic of at least some of said at least onevehicle history data variable.
 54. The apparatus of claim 48, whereinsaid at least one data grouping includes a plurality of data groupings.55. The apparatus of claim 48, wherein vehicle history data variablesformed within a data grouping are similar.
 56. The apparatus of claim55, wherein the similarity of the plurality of vehicle history datavariables within a data grouping includes being similar in at least oneof type and loss ratio value.
 57. The apparatus of claim 48, wherein theat least one data grouping includes at least one of: a Severe Problemdata grouping; a Potential Damage data grouping; a Ownership Historydata grouping; a Ownership Type data grouping; a Annual Mileage datagrouping; and a Potential Fraud data grouping.
 58. The apparatus ofclaim 48, wherein the overall value is indicative of the likelihood ofincurring future vehicle repair costs.
 59. The apparatus of claim 54,further comprising: means for generating a score based upon the overallvalue of each of the plurality of data groupings; and means for whereinsaid means for underwriting comprises means for underwriting aninsurance policy for the selected vehicle based on the score.
 60. Theapparatus of claim 59, wherein the score is characterized as at leastone of a number, mark, symbol, and color.
 61. The apparatus of claim 59,wherein said score is calculated based on a weighted combination ofoverall values of said plurality of data groupings.
 62. The apparatus ofclaim 61, wherein said score is the sum of weighted overall values ofeach of said plurality of data groupings represented as a mathematicalequation.
 63. The apparatus of claim 48, wherein the vehicle historydata variables include at least one of: Air Bag Deployed; Abandoned;Canadian Total Loss; Commercial Vehicle; Corporate Fleet Crash TestVehicle; Curbstoning Advisory; Damage Disclosure; Dismantled; DuplicateTitle Issued; Emissions Test; Estimated Current Mileage; ExceedsMechanical Limits; Exported Vehicle; Flood Advisory; Flood Damaged;Fire; Fire Damage; Frame Inspection; Grey Market Vehicle; GrossPolluter; Hail Damaged; Inconsistent Odometer Reading; Imported Vehicle;Junk; Last Owner—First Odometer Reading; Last Owner—First OdometerReading Date; Last Owner—Last Odometer Reading; Last Owner—Last OdometerReading Date; Last Owner—Average Annual Mileage; Last Owner—RecentAnnual Mileage; Last Owner—Last 12 Months Average Annual Mileage; LastOwner—Last 24 Months Average Annual Mileage; Last Owner—Last 36 MonthsAverage Annual Mileage; Last Owner Length of Ownership; Last Owner LienReported; Lease Vehicle; Manufacturer Buyback (Lemon); Not ActualMileage; Number of Owners; Number of Owners Footnote; Odometer ProblemFlag; Odometer Rollover; Personal; Police Accident Report; PoliceAccident Report Severe Damage; Police Vehicle; Potential Damage Flag;Potential Odometer Problem; Rebuilt Title Brand; Reconstructed TitleBrand; Recycle Vehicle; Rental Vehicle; Reported as Stolen; Repossessed;Safety Inspection Failed; Salvaged Auction; Severe Accident Flag; StolenVehicle Flag; Reported Stolen; Theft Recovery; Insurance Total Loss;Taxi Vehicle; Verified Odometer Rollback; and VIN Cloning Advisory. 64.The apparatus of claim 48, wherein said means for forming comprisesmeans for deriving a vehicle history data variable for at least one of:Number of Owners; Duplicate Title Issued; Estimated Current Mileage;Flood Advisory; Gray Market Vehicle Last Owner—Annual Mileage; LastOwner—Recent Annual Mileage; Last Owner—Length of Ownership; PotentialDamage Flag; Severe Accident Flag; and Insurance Total Loss.
 65. Anapparatus for rating an insurance policy comprising: means foridentifying a vehicle; means for forming at least one data groupingincluding of least one vehicle history data variable of the identifiedvehicle; means for processing the at least one vehicle history datavariable of the at least one data grouping; means for determining anoverall value of said at least one data grouping; and means for ratingan insurance policy for the identified vehicle based on said overallvalue of the at least one data grouping.
 66. The apparatus of claim 65,wherein the means for determining an overall value determines theoverall value based on whether a vehicle possesses or does not possess acharacteristic at least some of said at least one vehicle history datavariable.
 67. The apparatus of claim 65, wherein said at least one datagrouping includes a plurality of data groupings.
 68. The apparatus ofclaim 65, wherein vehicle history data variables formed within a datagrouping are similar.
 69. The apparatus of claim 68, wherein thesimilarity of vehicle history data variables within a data groupingincludes being similar in at least one of type and loss ratio value. 70.The apparatus of claim 65, wherein the at least one data groupingincludes one of: a Severe Problem data grouping; a Potential Damage datagrouping; a Ownership History data grouping; a Ownership Type datagrouping; a Annual Mileage data grouping; and a Potential Fraud datagrouping.
 71. The apparatus of claim 65, wherein the overall value isindicative of the likelihood of incurring future vehicle repair costs.72. The apparatus of claim 71, further comprising: means for generatinga score based upon an overall value of each of the plurality of the datagroupings; and wherein said means for rating comprises rating the policybased on the score.
 73. The apparatus of claim 72, wherein the score ischaracterized as one of a number, mark, symbol, and color.
 74. Theapparatus of claim 72, wherein said score is calculated based on aweighted combination of overall values of said plurality of datagroupings.
 75. The apparatus of claim 74, wherein said score is the sumof weighted overall values of each of said plurality of data groupingsrepresented as a mathematical equation.
 76. The apparatus of claim 65,wherein the vehicle history data variables include at least one of: AirBag Deployed; Abandoned; Canadian Total Loss; Commercial Vehicle;Corporate Fleet Crash Test Vehicle; Curbstoning Advisory; DamageDisclosure; Dismantled; Duplicate Title Issued; Emissions Test;Estimated Current Mileage; Exceeds Mechanical Limits; Exported Vehicle;Flood Advisory; Flood Damaged; Fire; Fire Damage; Frame Inspection; GreyMarket Vehicle; Gross Polluter; Hail Damaged; Inconsistent OdometerReading; Imported Vehicle; Junk; Last Owner First Odometer Reading; LastOwner—First Odometer Reading Date; Last Owner—Last Odometer Reading;Last Owner—Last Odometer Reading Date; Last Owner—Average AnnualMileage; Last Owner—Recent Annual Mileage; Last Owner—Last 12 MonthsAverage Annual Mileage; Last Owner—Last 24 Months Average AnnualMileage; Last Owner—Last 36 Months Average Annual Mileage; Last OwnerLength of Ownership; Last Owner Lien Reported; Lease Vehicle;Manufacturer Buyback (Lemon); Not Actual Mileage; Number of Owners;Number of Owners Footnote; Odometer Problem Flag; Odometer Rollover;Personal; Police Accident Report; Police Accident Report—Severe Damage;Police Vehicle; Potential Damage Flag; Potential Odometer Problem;Rebuilt Title Brand; Reconstructed Title Brand; Recycle Vehicle; RentalVehicle; Reported as Stolen; Repossessed; Safety Inspection Failed;Salvaged Auction; Severe Accident Flag; Stolen Vehicle Flag; ReportedStolen; Theft Recovery; Insurance Total Loss; Taxi Vehicle; VerifiedOdometer Rollback; and VIN Cloning Advisory.
 77. The apparatus of claim65, wherein said means for forming comprises means for deriving avehicle history data variable for at least one of: Number of Owners;Duplicate Title Issued; Estimated Current Mileage; Flood Advisory; GrayMarket Vehicle Last Owner—Annual Mileage; Last Owner—Recent AnnualMileage; Last Owner—Length of Ownership; Potential Damage Flag; SevereAccident Flag; and Insurance Total Loss.
 78. An apparatus fordetermining insurability of a vehicle comprising: means for identifyinga vehicle; means for forming at least one data grouping including atleast one vehicle history data variable of the identified vehicle; meansfor processing the at least one vehicle history data variable of the atleast one data grouping; means for determining an overall value of saidat least one data grouping; and means for calculating insurability ofthe identified vehicle based on said overall value of the at least onedata grouping.
 79. The apparatus of claim 78, wherein said means forcalculating comprises means for rating an insurance policy for theidentified vehicle based on said overall value of said at least one datagrouping.
 80. The apparatus of claim 78, wherein said means forcalculating comprises means for underwriting an insurance policy for theidentified vehicle based on said overall value of said at least one datagrouping.
 81. The apparatus of claim 78, wherein said at least one datagrouping includes a plurality of data groupings, said apparatus furthercomprising means for generating a score based upon an overall value ofeach of the plurality of data groupings and calculating insurability ofthe identified vehicle based on the score.
 82. The apparatus of claim81, wherein said score is calculated based on a weighted combination ofoverall values of said plurality of data groupings.
 83. The apparatus ofclaim 82, wherein said score is the sum of weighted overall values ofeach of said plurality of data groupings represented as a mathematicalequation.
 84. The apparatus of claim 78, wherein the means fordetermining an overall value comprises means for determining an overallvalue based on whether a vehicle possesses or does not possess acharacteristic of at least some of said at least one vehicle historydata variable.
 85. The apparatus of claim 78, wherein said at least onedata grouping includes a plurality of data groupings.
 86. The apparatusof claim 78, wherein vehicle history data variables formed within a datagrouping are similar.
 87. The apparatus of claim 86, wherein thesimilarity of the plurality of vehicle history data variables within adata grouping includes being similar in at least one of type and lossratio value.
 88. The apparatus of claim 78 wherein the at least one datagrouping includes at least one of. a Severe Problem data grouping; aPotential Damage data grouping; a Ownership History data grouping; aOwnership Type data grouping; a Annual Mileage data grouping; and aPotential Fraud data grouping.
 89. The apparatus of claim 78, whereinthe overall value is indicative of the likelihood of incurring futurevehicle repair costs.
 90. The apparatus of claim 81, wherein the scoreis characterized as at least one of one of a number, mark, symbol, andcolor.
 91. The apparatus of claim 81, wherein said score is calculatedbased on a weighted combination of overall values of said plurality ofdata groupings.
 92. The apparatus of claim 91, wherein said score is thesum of weighted overall values of each of said plurality of datagroupings represented as a mathematical equation.
 93. The apparatus ofclaim 78, wherein the vehicle history data variables include at leastone of: Air Bag Deployed; Abandoned; Canadian Total Loss; CommercialVehicle; Corporate Fleet Crash Test Vehicle; Curbstoning Advisory;Damage Disclosure; Dismantled; Duplicate Title Issued; Emissions Test;Estimated Current Mileage; Exceeds Mechanical Limits; Exported Vehicle;Flood Advisory; Flood Damaged; Fire; Fire Damage; Frame Inspection; GreyMarket Vehicle; Gross Polluter; Hail Damaged; Inconsistent OdometerReading; Imported Vehicle; Junk; Last Owner First Odometer Reading; LastOwner—First Odometer Reading Date; Last Owner—Last Odometer Reading;Last Owner—Last Odometer Reading Date; Last Owner—Average AnnualMileage; Last Owner—Recent Annual Mileage; Last Owner—Last 12 MonthsAverage Annual Mileage; Last Owner—Last 24 Months Average AnnualMileage; Last Owner—Last 36 Months Average Annual Mileage; Last OwnerLength of Ownership; Last Owner Lien Reported; Lease Vehicle;Manufacturer Buyback (Lemon); Not Actual Mileage; Number of Owners;Number of Owners Footnote; Odometer Problem Flag; Odometer Rollover;Personal; Police Accident Report; Police Accident Report—Severe Damage;Police Vehicle; Potential Damage Flag; Potential Odometer Problem;Rebuilt Title Brand; Reconstructed Title Brand; Recycle Vehicle; RentalVehicle; Reported as Stolen; Repossessed; Safety Inspection Failed;Salvaged Auction; Severe Accident Flag; Stolen Vehicle Flag; ReportedStolen; Theft Recovery; Insurance Total Loss; Taxi Vehicle; VerifiedOdometer Rollback; and VIN Cloning Advisory.
 94. The apparatus of claim78, wherein said means for forming comprises means for deriving avehicle history data variable for at least one of: Number of Owners;Duplicate Title Issued; Estimated Current Mileage; Flood Advisory; GrayMarket Vehicle Last Owner Annual Mileage; Last Owner Recent AnnualMileage; Last Owner—Length of Ownership; Potential Damage Flag; SevereAccident Flag; and insurance Total Loss.
 95. The apparatus of claim 48,wherein said means for identifying a vehicle, said means for forming atleast one data grouping including at least one vehicle history datavariable of the identified vehicle, said means for processing the atleast one vehicle history data variable of the at least one datagrouping, said means for determining an overall value of said at leastone data grouping, and said means for underwriting an insurance policyfor the identified vehicle based on said overall value of the at leastone data grouping comprises computer readable instructions recorded oncomputer readable media.
 96. The apparatus of claim 65, wherein saidmeans for identifying a vehicle, said means for forming at least onedata grouping including at least one vehicle history data variable ofthe identified vehicle, said means for processing the at least onevehicle history data variable of the at least one data grouping, saidmeans for determining an overall value of said at least one datagrouping, and said means for rating an insurance policy for theidentified vehicle based on said overall value of the at least one datagrouping comprises computer readable instructions recorded on computerreadable media.
 97. The apparatus of claim 78, wherein said means foridentifying a vehicle, said means for forming at least one data groupingincluding at least one vehicle history data variable of the identifiedvehicle, said means for processing the at least one vehicle history datavariable of the at least one data grouping, said means for determiningan overall value of said at least one data grouping, and said means forcalculating insurability for the identified vehicle based on saidoverall value of the at least one data grouping comprises computerreadable instructions recorded on computer readable media.